"... make no mistakes" :)

I imagine you can perform operations on query plans to transform them and determine equivalence?

`pg_hint_plan` has a debug log so you can verify Postgres actually used the hint or not! Used this during evaluations

I wouldn't be very excited about adding a 4B param model to my database deployment, but using this kind of approach while testing an app to identify query plans where Postgres is leaving performance on the table seems valuable without much risk.

Including the input parameters.

It's not unusual for us to end up with bad query plans because the shape of our data can vary pretty greatly. In many cases, a Foo has 1 Bar. But in some cases, a Foo has a million Bars. That can cause the query optimizer to treat lookups on the bar table as if there are few elements there (causing a scan instead of a seek).

For the general case, the optimizer gets it right. However, the fringe case is one that causes the entire system to crash. It's a bit akin to how an insertion sort can be faster than quick sort when n is small. The optimizer might make a bad assumption about the size of n which makes it pick an expensive n lookup when log(n) is available (but slower for small n).

I'd like to contribute my amateur hour entry into this thread, although I did administer and develop mssql stuff for awhile.

sure optimizations based on stats, but the stats are the wildcard, in my experience query plans can change suddenly.

Queries are translated into plans according to statistics. However the transforms will be deterministic and should only change one valid plan to another. I could very easily see a neural network manipulate transforms the same way the current programming does, its just that the neural networks are by nature really nicely suitable because the "decisions" are based on training, and this training can be closed world type things like the ai assists that chess engines are now getting. Obviously ai still can't play chess but apparently its very good at ranking board positions just by developing that much statistical info because its training comes not from reading the web, but playing a gazzilian games against itself in a "closed" chess world of its own.

I'm thinking that the ai does "this legal transform of the query plan should be applied to this pattern of data (statistics, cardinality, etc)" simply because the ai encountered it in closed world training, much like the chess thing.

Just a theory tho feel free to correct!

⇒ this may be useful for some workloads, but even then, can you afford to spend hours every now and then to update your 4B model to ensure it still picks a good query plan?

I think this would be likely comparable to a scheduled backup, so I think it would be an acceptable maintenance window. However, deterministic algorithms would likely beat re-training (or re-fine-tuning) the model. For example, one could analyze actual distributions or whatever (instead of assuming uniform), and then some plans would automatically be eliminated.

Imo a good thought experiment is to look at places that are hyper-optimized, like compilers. Would LLMs bring anything to the table (architecturally or performance-wise) to a piece of software that has been carefully crafted for decades? (Methinks no.)

I don't think anybody's denying that small models are doing distillation, the issue at hand is frontier lab vs frontier lab when it comes to big models.